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Speech Representation Learning Combining Conformer CPC with Deep Cluster for the ZeroSpeech Challenge 2021 ...
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Abstract:
We present a system for the Zero Resource Speech Challenge 2021, which combines a Contrastive Predictive Coding (CPC) with deep cluster. In deep cluster, we first prepare pseudo-labels obtained by clustering the outputs of a CPC network with k-means. Then, we train an additional autoregressive model to classify the previously obtained pseudo-labels in a supervised manner. Phoneme discriminative representation is achieved by executing the second-round clustering with the outputs of the final layer of the autoregressive model. We show that replacing a Transformer layer with a Conformer layer leads to a further gain in a lexical metric. Experimental results show that a relative improvement of 35% in a phonetic metric, 1.5% in the lexical metric, and 2.3% in a syntactic metric are achieved compared to a baseline method of CPC-small which is trained on LibriSpeech 460h data. We achieve top results in this challenge with the syntactic metric. ...
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Keyword:
Audio and Speech Processing eess.AS; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://dx.doi.org/10.48550/arxiv.2107.05899
https://arxiv.org/abs/2107.05899
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CHiME-6 Challenge: Tackling multispeaker speech recognition for unsegmented recordings
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In: CHiME 2020 - 6th International Workshop on Speech Processing in Everyday Environments ; https://hal.inria.fr/hal-02546993 ; CHiME 2020 - 6th International Workshop on Speech Processing in Everyday Environments, May 2020, Barcelona / Virtual, Spain (2020)
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